1. Field of the Invention
The present invention relates to an apparatus and a method for processing an input signal, an apparatus and a method for detecting a transmission characteristic of a room, an apparatus and a method for suppressing interferences, an apparatus and a method for detecting an inverse transmission characteristic of a room, an apparatus and a method for generating a prediction error signal, an apparatus and a method for retrieving a useful signal from an input signal, an apparatus and a method for suppressing an interference portion in a received signal. Particularly, the present invention relates to multidimensional adaptive filtering.
2. Description of the Related Art
In a signal propagation in a room between a sender and a receiver, such as in a wave propagation, it is often required to detect which influence the room has on the propagating waves by its room characteristic, for example a room impulse response. If the influence of the room is known, the same can be reproduced and/or reversed in the receiver, for example by adaptive filtering.
In order to determine the room characteristic, a transmitter transmits a signal known to a receiver, which is tapped off by a receiver. Based on a comparison between the transmitted and the tapped-off (detected) signal, the characteristic of a transmission channel between the transmitter and the receiver can be concluded, which results in a single-channel system (point to point connection).
Generally, several transmitters and several receivers can be positioned in the room, so that a multichannel system which several inputs and/or several outputs results, by which the room characteristic can be determined at the locations of the room determined by the arrangement of transmitters and receivers. Generally, these are so-called adaptive MIMO systems (MIMO=multiple input/multiple output). However, in these systems, only the relationships between the inputs and the outputs at discrete fixed room positions are considered, e.g. in the form of impulse responses or frequency responses. However, the field emitted by the transmitters is continuous and propagates in the form of wave fronts. Location-dependencies within the field are thus not considered in the prior art, since the received signals are processed directly based on an input/output description. In most applications, for example acoustics, only relatively few input channels of the adaptive system are assumed, as has been discussed in the paper J. Benesty and Y. Huang (eds.), Adaptive signal processing: Application to real-world problems, Springer-Verlag, Berlin, February 2003.
In a MIMO case of adaptive filtering according to the prior art, the following disadvantages result. On the one hand, the computing effort is very high due to the cross responses. For example, an adaptive filter with P input channels and Q output channels will adapt P Q responses and follow their changes. These individual responses can themselves have several hundred or thousand adaptive parameters, depending on the applications. To determine a room characteristic exactly, many input channels are required. With increasing number of input channels, convergence problems will occur, particularly with correlation between the input channels, such as has been described, for example, in the papers S. Shimauchi and S. Makino, “Stereo Projection Echo Canceller with True Echo Path Estimation”, Proc. IEEE International Conference on Acoustic, Speech, and Signal Processing ICASSP95, Detroit, Mich., USA, pages 3059-3062, May 1995, and J. Benesty, D. R. Morgan and M. M. Sondhi, “A better understanding and an improved solution to the problem of stereophonic acoustic echo cancellation”, Proc. IEEE International Conference on Acoustic, Speech, and Signal Processing ICASSP97, Munich, pp. 303-306, April 1997.
FIG. 20 shows an embodiment of a time discrete adaptive filter according to the prior art. The adaptive filter 2401 has L filter coefficients, which are combined to a vector h=[h (0), . . . , h(L−1)]. The filter 2401 has an input 2043 and an output 2405. An input signal u(n) is applied to the input 2403 of the filter 2401. An output signal y(n) is applied to the output 2405. The output 2405 is coupled to a summer 2407. The summer 2407 has a further input 2409, to which a signal d(n) is applied, as well as an output 2411 to which a signal e(n) is applied. A block 2413 is connected between the input 2403 of filter 2401 and the output 2411 of the summer 2407, wherein an adaptation algorithm for the filter coefficients is performed. Thus, block 2413 receives the signal u(n) as well as the signal e(n). Further, block 2413 has an output 2415 coupled to the filter 2401. The filter coefficients determined by the adaptation algorithm in the block 2413 are provided to the filter 2401 via the output 2415.
Adaptive time discrete filters of FIG. 20 represent a common technique in digital signal processing. The principle is to determine filter coefficients (combined to a vector h in the embodiment illustrated in FIG. 20) such that the output signal y(n) of the system (or an output channel in a multichannel system, respectively) is approximated to a desired signal d(n) or several desired signals, respectively, in a multichannel system at a known input signal u(n) (or several known input signals, respectively). This is achieved by block-wise minimization of the error signal e(n)=d(n)−y(n) or several error signals, respectively, in a multichannel system according to a predetermined criterion. A mean square error is, for example, used as criterion. The block length of the filter can be higher or equal to a sample. An optimization of the filter coefficients can further be performed recursively or non-recursively.
According to the prior art, the applications of adaptive filtering can be generally divided into four classes, as indicated in the paper of S. Haykin, Adaptive Filter Theory, 3. Ed., Prentice Hall Inc., Englewood Cliffs, N.J., USA, 1996. These are system identification, inverse modeling, prediction and interference suppression.
FIG. 21 shows a basic block diagram for single-channel system identification. The unknown system 2501, whose characteristic, such as an impulse response has to be determined, is excited via a system input 2503. Further, the unknown system 2501 has an output 2505, where a system output signal can be tapped off in response to an excitation signal. An adaptive filter 2507 is coupled to the system input 2503. The adaptive filter 2507 has an output 2509 as well as an adaptation input 2511.
A summer 2513 is disposed between the output 2509 of the adaptive filter 2507 and the output 2505 of the unknown system 2501, whose output is coupled to the input 2511 of the adaptive filter 2507.
As has already been mentioned, system identification is about determining the characteristic of the unknown system 2501, which can, for example, be a room, where the acoustic waves propagate. The characteristic of the room can, for example, be an impulse response which is characterized in the form of discrete impulse response coefficients, which can also be considered as filter coefficients. In order to determine the impulse response, the adaptive filter 2507 is excited in parallel to the unknown system 2501. An error signal e(n) is generated from the comparison of the systems applied to the respective output 2509 and 2505, based on which the adaptive filter 2507 is adapted. Thereby, the summer 2513 adds the output signal d(n) of the unknown system 2501 with an output signal y(n) valued with a negative sign. The result of this difference is supplied to the filter as error signal e(n). During adaptation, the filter coefficients are adapted for so long until the error signal e(n) is as low as possible. If e(n)=0, the coefficient set of the adaptive filter 2507 reflects exactly the impulse response of the unknown system 2501. In other words, after minimizing the error signal e(n), the modeling adaptive filter 2507 is optimally adapted to the unknown system 2501 (the system to be modeled) in the sense of the used optimization criteria, such as the criterion of the least-mean-error square. Apart from a single-channel system identification illustrated in FIG. 21, multichannel systems are identified, wherein, as has already been discussed, only discrete locations are considered. Such systems are described, for example, in S. Shimauchi and S. Makino, “Stereo Projection Echo Canceller with True Echo Path Estimation”, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP95, Detroit, Mich., USA, pages 3059-3062, May 1995 and in J. Benesty, D. R. Morgan, and M. M. Sondhi, “A better understanding and an improved solution to the problem of stereophonic acoustic echo cancellation”, Proc. IEEE International Conference on Acoustic, Speech, and Signal Processing ICASSP97, Munic, pages 303-306, April 1997.
In inverse modeling, the unknown system to be modeled is in series with the adaptive filter. FIG. 22 shows a basic block diagram of a system for inverse modeling.
The unknown system 2601 has an input 2603 and an output 2605. An adaptive filter 2607 is connected to the output 2605 of the unknown system 2601, which has an output 2609 as well as a further input 2611. The input 2603 of the unknown system 2601 is further coupled to a delay element 2613. The delay element 2613 has an output 2615 coupled to the output 2609 of the adaptive filter 2607 via a summer 2617. The summer 2617 has an outputs which is connected with the input 2611 of the filter 2607. In contrary to system identification, inverse modeling tries to reduce an influence of the unknown system 2601, for example its impulse response. Thereby, a difference is formed within the filter output signal and the system input signal. For considering a delay of the filter 2607 and the system 2601, optionally, a delay element 2613 can be provided in the reference branch. In inverse modeling according to FIG. 22, the system 2601 to be modeled is in series with the adaptive filter 2607. After minimizing the error signal e(n), the adaptive filter corresponds to the inverse unknown system in the optimum sense, depending on the used optimization criteria (for example the criterion of least mean error square). Apart from a single-channel inverse system modeling shown in FIG. 22, according to the prior art, in a multichannel case only discrete room positions are optimized, such as is described, for example, in the paper Masato Miyoshi, Yutaka Kaneda, “Inverse Filtering of Room Acoustics” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 2, February 1988.
FIG. 23 shows a block diagram of a prediction structure. The prediction structure has a delay member 2701 having an input 2703 and an output 2705. The output 2705 is coupled to an adaptive filter 2707, which has an output 2709 as well as a further input 2711. Parallel to the branch formed of delay member 2701 and adaptive filter 2707, is an adder 2713, whose input 2715 is connected to the input 2703 of the delay element 2701. Further, the adder 2713 has an output 2717 as well as a further input coupled to the output 2709 of the adaptive filter 2707.
In prediction, an estimate for a current signal value u(n) is determined from a number of past signal values, and a difference between the current value and the estimate, which is to be applied to the output 2709, is transmitted. In order to adaptively adjust the coefficients of the filter 2707, the difference signal applied to the output 2717 is supplied to the filter as reference for an adaptation of the filter coefficients. By this arrangement, it is obtained that the adaptive filter predicts the desired signal in an optimum way (corresponding to a used optimization criterion, such as a criterion of the least mean error square). Thus, only the unpredictable, i.e. the information carrying signal portion remains, which is transmitted as prediction error signal. In the receiver, an inverse operation is performed to retrieve the redundancy suppressed in the transmitter to reproduce the input signal as exactly as possible.
FIG. 24 shows a block diagram of a system for interference suppression according to the prior art. The system comprises an adaptive filter 2801 with an input 2803, an output 2805 as well as an adaptation input 2807. The output 2805 is coupled to an adder 2809. The adder 2809 has an output 2811 as well as an input 2813. The output 2811 of the adder 2809 is coupled to the adaptation input 2807 of the filter 2801.
Interference suppression according to claim 24 corresponds structurally to the basic concept of adaptive filtering according to FIG. 20, wherein the filter coefficients are adjusted in dependence on the used optimization criterion. Typically, a primary signal d(n) applied to the input 2813 of the adder 2809 consists of a mixture of useful signals and interference signals. A reference signal u(n) applied to the input 2803 of the filter 2801 is an estimate of the interference signal (the interference). Corresponding to an optimization criterion, such as a criterion of the least mean error square, the interference suppression minimizes the error signal e(n), which is a difference from the signal d(n) and the signal y(n). Thereby, the interference in the error signal is suppressed, which has the effect, in the ideal case, that useful signals are output and transmitted via the output 2811. Above that, the primary signal and the reference signal can be interchanged, so that the input signal of the adaptive filter corresponds to a mixture of useful signals and interference. This structure can be used in a location selective noise suppression. If the primary signal is set to zero, and a mixture of useful signals and interference signals is used as reference signal, statistical optimization criteria of blind source separation can be used. Such a concept is described in the paper A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, John Wiley & Sons, Inc., New York, 2001. The known approaches according to the prior art are thereby limited to single or multichannel interference reduction and blind source separation at a few determined discrete room positions (sensor locations by placing the room information at a system output).
It is a disadvantage of the known approaches according to the prior art that the emitted signals in the form of electromagnetical waves or in the form of acoustic sound waves are only detected and processed at a few discrete room positions. Information about the system is calculated based on the determined room properties at the discrete positions. However, this causes a significant determination error, if merely only a few sensors are positioned in the room to determine the room characteristic. To obtain a more specific determination of the room characteristic, a plurality of actors and sensors has to be positioned to discretize the room sufficiently. However, the computing effort increases significantly, since a system with high complexity has to be positioned, whose production and maintaining costs rise correspondingly.
It is a further disadvantage of the known concept that a continuous room characteristic, such as occurring during propagation of the electromagnetic waves can basically not be reproduced by the known systems. If the number of actors and sensors is increased to discretize the room further, the devices positioned in the room will have a significant influence on the detected room characteristic, since, for example, the echoes between the adjacent loudspeakers and microphones in the case of acoustic sound waves superpose the reflections caused by the room. These negative influences can only be eliminated approximately by complex compensation algorithms.
It is a further disadvantage of the multichannel concepts according to the prior art that the conventional approaches are in the way of an efficient implementation of wave-field synthesis or wave-field analysis. In wave-field synthesis, for example with a plurality of loudspeakers, which are idealized as spherical antenna, an acoustic sound field in a room where the loudspeaker are positioned can be reproduced exactly and at every location of the room and at any time. Therefore, however, it is required to be able to determine the room characteristic of the room where the loudspeakers are positioned also at any location. Since the conventional approaches only allow a characterization at discrete locations, it is basically not possible to reproduce the desired acoustic sound field exactly at every location of the room with the help of wave-field synthesis by using the standard concept for detecting the room characteristic.